Papers by Pu Ren
Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)
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| Challenge: | Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction. |
| Approach: | They propose a visual question answering task that provides a natural language answer to a question based on a given image and an automated pipeline to generate ambiguity-clarification question pairs. |
| Outcome: | The proposed benchmark targets three common categories of ambiguity in visual question answering (VQA) context and encompasses various VQA scenarios. |
Insert or Attach: Taxonomy Completion via Box Embedding (2024.acl-long)
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| Challenge: | Existing taxonomy expansion methods embed concepts as vectors in Euclidean space, causing incorrectly model asymmetric relations. |
| Approach: | They propose to use box containment and center closeness to create geometric scorers that capture intrinsic relationships between concepts. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets. |
Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference (2026.acl-long)
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| Challenge: | Existing methods for large-scale modeling memorize sensitive information . however, they are limited in real-world scenarios and require updating parameters . |
| Approach: | They propose a training-free, plug-and-play inference-time unlearning strategy that uses a probe to detect queries involving forgettable concepts and applies entropy-guided decoding to suppress target knowledge. |
| Outcome: | Experiments on MUSE, RWKU, and WMDP datasets show that SEGUE outperforms existing methods. |
Model Balancing Helps Low-data Training and Fine-tuning (2024.emnlp-main)
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| Challenge: | Recent advances in foundation models have emphasized the need to align pre-trained models with specialized domains using small, curated datasets. |
| Approach: | They propose a layer-wise learning rate scheduler that balances training quality across layers . they adapt it to a curated dataset to achieve alignment with specialized domains . |
| Outcome: | The proposed model shows that it can be used to balance training quality across layers and improve low-data training and fine-tuning for both NLP and SciML tasks. |
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus. |
| Approach: | They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling . |
| Outcome: | The proposed network achieves state-of-the-art on several DS-NER benchmarks. |
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)
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| Challenge: | Existing methods to train unbiased methods such as REINFORCE take time to train. |
| Approach: | They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets. |
| Outcome: | The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training. |
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in text-only "slow thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs). |
| Approach: | They propose a VRM Reflection-V which enhances visual reflection based on reasoning data for cold-start and reward design for reinforcement learning. |
| Outcome: | The proposed model improves visual reflection for cold-start and reward design for reinforcement learning (RL) it maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities. |